Code Plateau Fellowship; Data Science Track Competition¶

Context:¶

The Tanzanian tourism sector plays a significant role in the Tanzanian economy, contributing about 17% to the country’s GDP and 25% of all foreign exchange revenues. The sector, which provides direct employment for more than 600,000 people and up to 2 million people indirectly, generated approximately $2.4 billion in 2018 according to government statistics. Tanzania received a record 1.1 million international visitor arrivals in 2014, mostly from Europe, the US and Africa. Tanzania is the only country in the world which has allocated more than 25% of its total area for wildlife, national parks, and protected areas.There are 16 national parks in Tanzania, 28 game reserves, 44 game-controlled areas, two marine parks and one conservation area.

Objective:¶

The objective of this competition is to explore and build a linear regression model that will predict the spending behaivior of tourists visiting Tanzania.The model can be used by different tour operators and the Tanzania Tourism Board to automatically help tourists across the world estimate their expenditure before visiting Tanzania.

Data Description¶

The dataset describes 6476 rows of up-to-date information on tourist expenditure collected by the National Bureau of Statistics (NBS) in Tanzania.The dataset was collected to gain a better understanding of the status of the tourism sector and provide an instrument that will enable sector growth. The survey covers seven departure points, namely: Julius Nyerere International Airport, Kilimanjaro International Airport, Abeid Amani Karume International Airport, and the Namanga, Tunduma, Mtukula and Manyovu border points.

Importing necessary libraries and data¶

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
tz = pd.read_csv('Train .csv')
In [4]:
tz.head(10)
Out[4]:
ID country age_group travel_with total_female total_male purpose main_activity info_source tour_arrangement ... package_transport_tz package_sightseeing package_guided_tour package_insurance night_mainland night_zanzibar payment_mode first_trip_tz most_impressing total_cost
0 tour_0 SWIZERLAND 45-64 Friends/Relatives 1.0 1.0 Leisure and Holidays Wildlife tourism Friends, relatives Independent ... No No No No 13.0 0.0 Cash No Friendly People 674602.5
1 tour_10 UNITED KINGDOM 25-44 NaN 1.0 0.0 Leisure and Holidays Cultural tourism others Independent ... No No No No 14.0 7.0 Cash Yes Wonderful Country, Landscape, Nature 3214906.5
2 tour_1000 UNITED KINGDOM 25-44 Alone 0.0 1.0 Visiting Friends and Relatives Cultural tourism Friends, relatives Independent ... No No No No 1.0 31.0 Cash No Excellent Experience 3315000.0
3 tour_1002 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 11.0 0.0 Cash Yes Friendly People 7790250.0
4 tour_1004 CHINA 1-24 NaN 1.0 0.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Independent ... No No No No 7.0 4.0 Cash Yes No comments 1657500.0

5 rows × 23 columns

Data Overview¶

  • Observations
  • Sanity checks
In [2]:
tz.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4809 entries, 0 to 4808
Data columns (total 23 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   ID                     4809 non-null   object 
 1   country                4809 non-null   object 
 2   age_group              4809 non-null   object 
 3   travel_with            3695 non-null   object 
 4   total_female           4806 non-null   float64
 5   total_male             4804 non-null   float64
 6   purpose                4809 non-null   object 
 7   main_activity          4809 non-null   object 
 8   info_source            4809 non-null   object 
 9   tour_arrangement       4809 non-null   object 
 10  package_transport_int  4809 non-null   object 
 11  package_accomodation   4809 non-null   object 
 12  package_food           4809 non-null   object 
 13  package_transport_tz   4809 non-null   object 
 14  package_sightseeing    4809 non-null   object 
 15  package_guided_tour    4809 non-null   object 
 16  package_insurance      4809 non-null   object 
 17  night_mainland         4809 non-null   float64
 18  night_zanzibar         4809 non-null   float64
 19  payment_mode           4809 non-null   object 
 20  first_trip_tz          4809 non-null   object 
 21  most_impressing        4496 non-null   object 
 22  total_cost             4809 non-null   float64
dtypes: float64(5), object(18)
memory usage: 864.2+ KB
In [3]:
tz.isnull().sum()
Out[3]:
ID                          0
country                     0
age_group                   0
travel_with              1114
total_female                3
total_male                  5
purpose                     0
main_activity               0
info_source                 0
tour_arrangement            0
package_transport_int       0
package_accomodation        0
package_food                0
package_transport_tz        0
package_sightseeing         0
package_guided_tour         0
package_insurance           0
night_mainland              0
night_zanzibar              0
payment_mode                0
first_trip_tz               0
most_impressing           313
total_cost                  0
dtype: int64

There are four categories with missing values

In [24]:
sns.pairplot(tz, hue='country')
Out[24]:
<seaborn.axisgrid.PairGrid at 0x11ef33692b0>
In [25]:
sns.distplot(tz['total_cost']).set(title = 'Spending distribution')
C:\Users\user\Downloads\New folder\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
Out[25]:
[Text(0.5, 1.0, 'Spending distribution')]
In [26]:
sns.heatmap(tz.corr(), annot= True)
Out[26]:
<AxesSubplot:>

Exploratory Data Analysis (EDA)¶

  • EDA is an important part of any project involving data.
  • It is important to investigate and understand the data better before building a model with it.
  • A few questions have been mentioned below which will help you approach the analysis in the right manner and generate insights from the data.
  • A thorough analysis of the data, in addition to the questions mentioned below, should be done.

Questions:

  1. What are the top 5 countries with the highest spending statistic ?

  2. which age-group are the highest spenders and who are the over all highest spenders by travel with?

  3. which country are have the most spending tourists?

  4. what is the average number of nights a toursits spends in Tanzania mainland?

  5. what is the average number of nights a toursits spends in Zanzibar?

  6. what is the most prefered payment mode by tourists?

  7. Highlight the Aspects of tourism that are more profitable and in which it is worthwhile to invest in

  8. what is the most sort after food by tourists?

In [4]:
#top 5 countries with highest spending statistics
countries = tz[['country', 'total_cost']].head(10).groupby('country').sum()
countries
Out[4]:
total_cost
country
CHINA 1657500.0
INDIA 2486250.0
NIGERIA 994500.0
SOUTH AFRICA 466140.0
SWIZERLAND 674602.5
UNITED KINGDOM 14441106.5
UNITED STATES OF AMERICA 3480750.0
In [91]:
plt.style.use('seaborn')
countries.plot(color='black', title= 'TOP 7 COUNTRIES', kind='bar')
Out[91]:
<AxesSubplot:title={'center':'TOP 7 COUNTRIES'}, xlabel='country'>
In [8]:
#Age group with the highest spenders
Age = tz[['age_group', 'total_cost', 'travel_with']]
Age
Out[8]:
age_group total_cost travel_with
0 45-64 674602.5 Friends/Relatives
1 25-44 3214906.5 NaN
2 25-44 3315000.0 Alone
3 25-44 7790250.0 Spouse
4 1-24 1657500.0 NaN
... ... ... ...
4804 45-64 3315000.0 Alone
4805 25-44 10690875.0 Spouse
4806 1-24 2246636.7 NaN
4807 25-44 1160250.0 Friends/Relatives
4808 25-44 13260000.0 Spouse

4809 rows × 3 columns

In [9]:
Age_group = Age.groupby('age_group').sum()
Age_group
Out[9]:
total_cost
age_group
1-24 3.379088e+09
25-44 1.498710e+10
45-64 1.537184e+10
65+ 5.284068e+09
In [10]:
Age_group.plot(kind='bar', title='HIGHEST SPENDING AGE GROUP')
Out[10]:
<AxesSubplot:title={'center':'HIGHEST SPENDING AGE GROUP'}, xlabel='age_group'>
In [14]:
Age_count = Age[['age_group']].value_counts()
Age_count
Out[14]:
age_group
25-44        2487
45-64        1391
1-24          624
65+           307
dtype: int64
In [15]:
#Country with the most spending tourist
cst =tz[['country', 'total_cost']].head(10)
cst
Out[15]:
country total_cost
0 SWIZERLAND 674602.5
1 UNITED KINGDOM 3214906.5
2 UNITED KINGDOM 3315000.0
3 UNITED KINGDOM 7790250.0
4 CHINA 1657500.0
5 UNITED KINGDOM 120950.0
6 SOUTH AFRICA 466140.0
7 UNITED STATES OF AMERICA 3480750.0
8 NIGERIA 994500.0
9 INDIA 2486250.0
In [16]:
cst.plot(kind = 'bar', title = 'Country with the most spending tourist')
Out[16]:
<AxesSubplot:title={'center':'Country with the most spending tourist'}>
In [17]:
#Average number of nights tourists spend at Tanzania Mainland
tz[['night_mainland']].mean()
Out[17]:
night_mainland    8.488043
dtype: float64

An average of eight(8) nights are spent by tourists on Tanzania mainland

In [18]:
#Average number of nights tourists spend at Tanzania Mainland
tz[['night_zanzibar']].mean()
Out[18]:
night_zanzibar    2.304429
dtype: float64

An average of two(2) nights are spent by tourists in Zanzibar

In [19]:
#Most preferred mode of payment by tourists
sns.catplot(data=tz, x= 'payment_mode', kind='count')
Out[19]:
<seaborn.axisgrid.FacetGrid at 0x11eecff2400>

The most preferred mode of payment by the tourists is cash

In [20]:
#Aspects of tourism that are more profitable and in which it is worthwhile to invest in
sns.catplot(data= tz, y= 'main_activity', kind='count')
Out[20]:
<seaborn.axisgrid.FacetGrid at 0x11eece7ac40>

The aspect of tourism that is more profitable and in which it is worthwhile to invest in is Wildlife tourism

In [22]:
#The most sort after food
sns.catplot(data=tz, x='package_food', kind='count' )
Out[22]:
<seaborn.axisgrid.FacetGrid at 0x11eece7a040>

The most sort after food by the tourists is non package_food

Data Preprocessing¶

  • Missing value treatment (if needed)
  • Feature engineering
  • Outlier detection and treatment (if needed)
  • Preparing data for modeling
  • Feature importance
  • scaling
In [56]:
#Missing values in travel_with
tz['travel_with'].fillna(value='Alone', inplace= True)
In [57]:
tz
Out[57]:
ID country age_group travel_with total_female total_male purpose main_activity info_source tour_arrangement ... package_transport_tz package_sightseeing package_guided_tour package_insurance night_mainland night_zanzibar payment_mode first_trip_tz most_impressing total_cost
0 tour_0 SWIZERLAND 45-64 Friends/Relatives 1.0 1.0 Leisure and Holidays Wildlife tourism Friends, relatives Independent ... No No No No 13.0 0.0 Cash No Friendly People 674602.5
1 tour_10 UNITED KINGDOM 25-44 Alone 1.0 0.0 Leisure and Holidays Cultural tourism others Independent ... No No No No 14.0 7.0 Cash Yes Wonderful Country, Landscape, Nature 3214906.5
2 tour_1000 UNITED KINGDOM 25-44 Alone 0.0 1.0 Visiting Friends and Relatives Cultural tourism Friends, relatives Independent ... No No No No 1.0 31.0 Cash No Excellent Experience 3315000.0
3 tour_1002 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 11.0 0.0 Cash Yes Friendly People 7790250.0
4 tour_1004 CHINA 1-24 Alone 1.0 0.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Independent ... No No No No 7.0 4.0 Cash Yes No comments 1657500.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4804 tour_993 UAE 45-64 Alone 0.0 1.0 Business Hunting tourism Friends, relatives Independent ... No No No No 2.0 0.0 Credit Card No No comments 3315000.0
4805 tour_994 UNITED STATES OF AMERICA 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes Yes 11.0 0.0 Cash Yes Friendly People 10690875.0
4806 tour_995 NETHERLANDS 1-24 Alone 1.0 0.0 Leisure and Holidays Wildlife tourism others Independent ... No No No No 3.0 7.0 Cash Yes Good service 2246636.7
4807 tour_997 SOUTH AFRICA 25-44 Friends/Relatives 1.0 1.0 Business Beach tourism Travel, agent, tour operator Independent ... No No No No 5.0 0.0 Credit Card No Friendly People 1160250.0
4808 tour_999 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 4.0 7.0 Cash Yes Friendly People 13260000.0

4809 rows × 23 columns

In [58]:
#Missing values for total_male
tz['total_male'].fillna(method= 'bfill', inplace = True)
In [59]:
tz
Out[59]:
ID country age_group travel_with total_female total_male purpose main_activity info_source tour_arrangement ... package_transport_tz package_sightseeing package_guided_tour package_insurance night_mainland night_zanzibar payment_mode first_trip_tz most_impressing total_cost
0 tour_0 SWIZERLAND 45-64 Friends/Relatives 1.0 1.0 Leisure and Holidays Wildlife tourism Friends, relatives Independent ... No No No No 13.0 0.0 Cash No Friendly People 674602.5
1 tour_10 UNITED KINGDOM 25-44 Alone 1.0 0.0 Leisure and Holidays Cultural tourism others Independent ... No No No No 14.0 7.0 Cash Yes Wonderful Country, Landscape, Nature 3214906.5
2 tour_1000 UNITED KINGDOM 25-44 Alone 0.0 1.0 Visiting Friends and Relatives Cultural tourism Friends, relatives Independent ... No No No No 1.0 31.0 Cash No Excellent Experience 3315000.0
3 tour_1002 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 11.0 0.0 Cash Yes Friendly People 7790250.0
4 tour_1004 CHINA 1-24 Alone 1.0 0.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Independent ... No No No No 7.0 4.0 Cash Yes No comments 1657500.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4804 tour_993 UAE 45-64 Alone 0.0 1.0 Business Hunting tourism Friends, relatives Independent ... No No No No 2.0 0.0 Credit Card No No comments 3315000.0
4805 tour_994 UNITED STATES OF AMERICA 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes Yes 11.0 0.0 Cash Yes Friendly People 10690875.0
4806 tour_995 NETHERLANDS 1-24 Alone 1.0 0.0 Leisure and Holidays Wildlife tourism others Independent ... No No No No 3.0 7.0 Cash Yes Good service 2246636.7
4807 tour_997 SOUTH AFRICA 25-44 Friends/Relatives 1.0 1.0 Business Beach tourism Travel, agent, tour operator Independent ... No No No No 5.0 0.0 Credit Card No Friendly People 1160250.0
4808 tour_999 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 4.0 7.0 Cash Yes Friendly People 13260000.0

4809 rows × 23 columns

In [60]:
#Missing values for total_female
tz['total_female'].fillna(method='bfill', inplace = True)
In [61]:
tz
Out[61]:
ID country age_group travel_with total_female total_male purpose main_activity info_source tour_arrangement ... package_transport_tz package_sightseeing package_guided_tour package_insurance night_mainland night_zanzibar payment_mode first_trip_tz most_impressing total_cost
0 tour_0 SWIZERLAND 45-64 Friends/Relatives 1.0 1.0 Leisure and Holidays Wildlife tourism Friends, relatives Independent ... No No No No 13.0 0.0 Cash No Friendly People 674602.5
1 tour_10 UNITED KINGDOM 25-44 Alone 1.0 0.0 Leisure and Holidays Cultural tourism others Independent ... No No No No 14.0 7.0 Cash Yes Wonderful Country, Landscape, Nature 3214906.5
2 tour_1000 UNITED KINGDOM 25-44 Alone 0.0 1.0 Visiting Friends and Relatives Cultural tourism Friends, relatives Independent ... No No No No 1.0 31.0 Cash No Excellent Experience 3315000.0
3 tour_1002 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 11.0 0.0 Cash Yes Friendly People 7790250.0
4 tour_1004 CHINA 1-24 Alone 1.0 0.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Independent ... No No No No 7.0 4.0 Cash Yes No comments 1657500.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4804 tour_993 UAE 45-64 Alone 0.0 1.0 Business Hunting tourism Friends, relatives Independent ... No No No No 2.0 0.0 Credit Card No No comments 3315000.0
4805 tour_994 UNITED STATES OF AMERICA 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes Yes 11.0 0.0 Cash Yes Friendly People 10690875.0
4806 tour_995 NETHERLANDS 1-24 Alone 1.0 0.0 Leisure and Holidays Wildlife tourism others Independent ... No No No No 3.0 7.0 Cash Yes Good service 2246636.7
4807 tour_997 SOUTH AFRICA 25-44 Friends/Relatives 1.0 1.0 Business Beach tourism Travel, agent, tour operator Independent ... No No No No 5.0 0.0 Credit Card No Friendly People 1160250.0
4808 tour_999 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 4.0 7.0 Cash Yes Friendly People 13260000.0

4809 rows × 23 columns

In [62]:
#Missing values for most_impressing
tz['most_impressing'].fillna(value='no comments', inplace= True)
In [63]:
tz
Out[63]:
ID country age_group travel_with total_female total_male purpose main_activity info_source tour_arrangement ... package_transport_tz package_sightseeing package_guided_tour package_insurance night_mainland night_zanzibar payment_mode first_trip_tz most_impressing total_cost
0 tour_0 SWIZERLAND 45-64 Friends/Relatives 1.0 1.0 Leisure and Holidays Wildlife tourism Friends, relatives Independent ... No No No No 13.0 0.0 Cash No Friendly People 674602.5
1 tour_10 UNITED KINGDOM 25-44 Alone 1.0 0.0 Leisure and Holidays Cultural tourism others Independent ... No No No No 14.0 7.0 Cash Yes Wonderful Country, Landscape, Nature 3214906.5
2 tour_1000 UNITED KINGDOM 25-44 Alone 0.0 1.0 Visiting Friends and Relatives Cultural tourism Friends, relatives Independent ... No No No No 1.0 31.0 Cash No Excellent Experience 3315000.0
3 tour_1002 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 11.0 0.0 Cash Yes Friendly People 7790250.0
4 tour_1004 CHINA 1-24 Alone 1.0 0.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Independent ... No No No No 7.0 4.0 Cash Yes No comments 1657500.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4804 tour_993 UAE 45-64 Alone 0.0 1.0 Business Hunting tourism Friends, relatives Independent ... No No No No 2.0 0.0 Credit Card No No comments 3315000.0
4805 tour_994 UNITED STATES OF AMERICA 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes Yes 11.0 0.0 Cash Yes Friendly People 10690875.0
4806 tour_995 NETHERLANDS 1-24 Alone 1.0 0.0 Leisure and Holidays Wildlife tourism others Independent ... No No No No 3.0 7.0 Cash Yes Good service 2246636.7
4807 tour_997 SOUTH AFRICA 25-44 Friends/Relatives 1.0 1.0 Business Beach tourism Travel, agent, tour operator Independent ... No No No No 5.0 0.0 Credit Card No Friendly People 1160250.0
4808 tour_999 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 4.0 7.0 Cash Yes Friendly People 13260000.0

4809 rows × 23 columns

In [64]:
from pandas_profiling import ProfileReport
profile = ProfileReport(tz)
profile
Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[64]:

EDA¶

  • It is a good idea to explore the data once again after manipulating it.

PREPARING DATA FOR MODELLING¶

In [65]:
#features for modelling
x = tz[['total_female',
       'total_male','night_mainland','night_zanzibar'
       ]]
In [66]:
#Target variable or Prediction variable.
y = tz[['total_cost']]
In [67]:
#Train test split
from sklearn.model_selection import train_test_split
In [68]:
x_test,x_train, y_test,y_train = train_test_split(x,y, test_size=0.8, random_state=105)

Building a Regression Model¶

In [69]:
#Creating and Training the model
from sklearn.linear_model import LinearRegression
In [70]:
#Instantiate model
lm = LinearRegression()
In [71]:
lm.fit(x_train,y_train)
Out[71]:
LinearRegression()
In [72]:
print(lm.intercept_)
[4283710.80386905]
In [73]:
lm.coef_
Out[73]:
array([[2142343.47631708,  693100.12934699,   50978.34380813,
         362964.92797582]])
In [74]:
x_train.columns
Out[74]:
Index(['total_female', 'total_male', 'night_mainland', 'night_zanzibar'], dtype='object')

Testing Linear Model Assumptions¶

In [75]:
prediction = lm.predict(x_test)
prediction
Out[75]:
array([[ 5333659.33987294],
       [10016757.31202075],
       [ 9659908.90536385],
       [ 5180724.30844856],
       [16425606.4751851 ],
       [ 6680945.99922677],
       [ 5594667.5802325 ],
       [ 7190729.43730806],
       [ 5180724.30844856],
       [11621552.05534841],
       [13788160.6701879 ],
       [ 8189699.62950382],
       [ 5078767.6208323 ],
       [15762719.70750957],
       [ 7886445.93158824],
       [ 9398900.66500429],
       [ 6782902.68684303],
       [12962039.34868405],
       [ 9312476.22965833],
       [ 7323067.78476564],
       [12126271.06173319],
       [ 5435616.0274892 ],
       [ 9720302.98012336],
       [10209489.73423784],
       [ 9659908.90536385],
       [ 5486594.37129733],
       [12525565.87269938],
       [ 6167662.40475976],
       [ 8183784.05493818],
       [34597173.64556116],
       [ 5771867.75017929],
       [ 6353226.21603552],
       [ 9618346.2925071 ],
       [ 5741486.09033797],
       [ 9264114.25078339],
       [ 5690507.74652984],
       [ 6680945.99922677],
       [ 6383607.87587684],
       [ 6731924.3430349 ],
       [ 9105062.69804009],
       [15914120.75136554],
       [ 5078767.6208323 ],
       [ 7883829.56665505],
       [27330088.92757099],
       [ 7814702.60564344],
       [ 9659908.90536385],
       [10677027.7147631 ],
       [11147214.70880496],
       [ 5333659.33987294],
       [ 6884859.37445929],
       [10415851.17623368],
       [ 7577959.50380628],
       [ 5690507.74652984],
       [ 7496599.50015683],
       [ 5180724.30844856],
       [ 4976810.93321604],
       [11003695.40833195],
       [ 7667884.09553797],
       [ 7883829.56665505],
       [ 9698453.25978088],
       [ 5822846.09398742],
       [ 6629967.65541864],
       [10921933.51117596],
       [10464381.45327848],
       [ 8189699.62950382],
       [10107533.04662158],
       [ 6528010.96780239],
       [ 5822846.09398742],
       [14911449.45603077],
       [10877473.58219329],
       [ 7955404.59442999],
       [ 5027789.27702417],
       [ 5282680.99606481],
       [10263084.44297916],
       [ 9392784.14368537],
       [ 6506161.2474599 ],
       [ 5078767.6208323 ],
       [ 5180724.30844856],
       [ 5180724.30844856],
       [ 9282094.56981701],
       [ 9296943.97738803],
       [14542648.95792877],
       [22385267.22178095],
       [ 8546748.98291399],
       [ 6629967.65541864],
       [ 7139751.09349993],
       [ 7877913.9920894 ],
       [ 7832851.22284692],
       [13146890.02751817],
       [17962158.04895369],
       [11109152.25233394],
       [ 7425024.4723819 ],
       [ 5078767.6208323 ],
       [13947011.27617793],
       [ 8360984.22488496],
       [ 6680945.99922677],
       [10016757.31202075],
       [ 7425024.4723819 ],
       [ 6782902.68684303],
       [ 6629967.65541864],
       [ 7323067.78476564],
       [ 7845084.26548476],
       [ 5231702.65225669],
       [10640730.48035613],
       [11745157.51655389],
       [ 7139751.09349993],
       [ 6986816.06207555],
       [ 5027789.27702417],
       [ 7482119.33750894],
       [ 7985786.25427131],
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In [76]:
sns.distplot(y_test- prediction/10**6)
C:\Users\user\Downloads\New folder\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
Out[76]:
<AxesSubplot:ylabel='Density'>
In [77]:
#Looking at the Linear evaluation metrics we get:
from sklearn import metrics
In [78]:
#Average error of the model
metrics.mean_absolute_error(y_test,prediction)
Out[78]:
7159079.5603594845
In [79]:
#Checkmating target errors by squaring
metrics.mean_squared_error(y_test,prediction)
Out[79]:
114911704866686.25
In [80]:
np.sqrt(metrics.mean_squared_error(y_test,prediction))
Out[80]:
10719687.722442582
In [81]:
r_squared = lm.score(x_test, prediction)
r_squared
Out[81]:
1.0
In [82]:
from sklearn.metrics import r2_score
In [83]:
r2_score(y_test,prediction)
Out[83]:
0.10424171803690241
In [84]:
from sklearn.svm import SVC
In [85]:
tz2= pd.read_csv('Test .csv')
tz2.head(20)
Out[85]:
ID country age_group travel_with total_female total_male purpose main_activity info_source tour_arrangement ... package_food package_transport_tz package_sightseeing package_guided_tour package_insurance night_mainland night_zanzibar payment_mode first_trip_tz most_impressing
0 tour_1 AUSTRALIA 45-64 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes Yes Yes 10 3 Cash Yes Wildlife
1 tour_100 SOUTH AFRICA 25-44 Friends/Relatives 0.0 4.0 Business Wildlife tourism Tanzania Mission Abroad Package Tour ... No No No No No 13 0 Cash No Wonderful Country, Landscape, Nature
2 tour_1001 GERMANY 25-44 Friends/Relatives 3.0 0.0 Leisure and Holidays Beach tourism Friends, relatives Independent ... No No No No No 7 14 Cash No No comments
3 tour_1006 CANADA 24-Jan Friends/Relatives 2.0 0.0 Leisure and Holidays Cultural tourism others Independent ... No No No No No 0 4 Cash Yes Friendly People
4 tour_1009 UNITED KINGDOM 45-64 Friends/Relatives 2.0 2.0 Leisure and Holidays Wildlife tourism Friends, relatives Package Tour ... Yes Yes No No No 10 0 Cash Yes Friendly People
5 tour_1010 DENMARK 25-44 NaN 1.0 0.0 Volunteering Cultural tourism Friends, relatives Independent ... No No No No No 55 0 Cash No Good service
6 tour_1014 GERMANY 45-64 NaN 0.0 1.0 Meetings and Conference Mountain climbing Travel, agent, tour operator Package Tour ... Yes Yes No Yes No 9 0 Cash Yes Friendly People
7 tour_1015 RUSSIA 24-Jan Alone 1.0 0.0 Visiting Friends and Relatives Beach tourism Friends, relatives Independent ... No No No No No 26 3 Cash No Friendly People
8 tour_1019 GERMANY 24-Jan Alone 1.0 0.0 Leisure and Holidays Cultural tourism Friends, relatives Independent ... No No No No No 3 0 Cash No Friendly People
9 tour_1020 FRANCE 24-Jan Friends/Relatives 1.0 1.0 Leisure and Holidays Wildlife tourism Newspaper, magazines,brochures Independent ... No No No No No 30 9 Cash Yes Friendly People
10 tour_1023 SPAIN 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes No No Yes 6 4 Cash Yes Wildlife
11 tour_1025 UNITED KINGDOM 25-44 Spouse and Children 1.0 2.0 Leisure and Holidays Wildlife tourism Radio, TV, Web Package Tour ... Yes Yes Yes Yes No 6 6 Cash Yes Friendly People
12 tour_1031 SWIZERLAND 45-64 Spouse and Children 4.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes Yes No 10 7 Cash Yes Wildlife
13 tour_1037 UNITED STATES OF AMERICA 24-Jan Friends/Relatives 1.0 1.0 Scientific and Academic Cultural tourism Friends, relatives Independent ... No No No No No 30 0 Cash No No comments
14 tour_1044 GERMANY 25-44 Alone 0.0 1.0 Leisure and Holidays business Friends, relatives Package Tour ... Yes Yes No No No 6 0 Cash Yes No comments
15 tour_1053 CANADA 45-64 Spouse and Children 4.0 1.0 Visiting Friends and Relatives Wildlife tourism Radio, TV, Web Independent ... No No No No No 21 0 Cash No Friendly People
16 tour_1056 SPAIN 25-44 Friends/Relatives 0.0 2.0 Leisure and Holidays Wildlife tourism Newspaper, magazines,brochures Independent ... No No No No No 10 6 Cash Yes Wonderful Country, Landscape, Nature
17 tour_106 CHINA 25-44 Friends/Relatives 2.0 2.0 Visiting Friends and Relatives Mountain climbing Trade fair Package Tour ... Yes Yes No No No 7 0 Cash No No comments
18 tour_1064 UNITED KINGDOM 25-44 Spouse and Children 4.0 1.0 Visiting Friends and Relatives Beach tourism Friends, relatives Independent ... No No No No No 30 0 Cash No Wonderful Country, Landscape, Nature
19 tour_1069 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Radio, TV, Web Package Tour ... No Yes Yes Yes No 6 7 Cash Yes Wildlife

20 rows × 22 columns

In [86]:
tz2.corr()
Out[86]:
total_female total_male night_mainland night_zanzibar
total_female 1.000000 0.288933 0.015265 0.078020
total_male 0.288933 1.000000 -0.035880 0.020622
night_mainland 0.015265 -0.035880 1.000000 0.516262
night_zanzibar 0.078020 0.020622 0.516262 1.000000
In [87]:
sns.heatmap(tz2.corr(), annot= True)
Out[87]:
<AxesSubplot:>
In [88]:
lm.fit(x,y)
Out[88]:
LinearRegression()
In [90]:
y_pred = lm.predict(x_test)
y_pred
Out[90]:
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Actionable Insights and Recommendations¶

**-After deep analysis, we observed that the most preferred mode of payment by the tourists is cash. We recommend that the Government of Tanzania should create a limit for the possession of cash on one's self, to avoid instances of robbery and pick-pocketing.

**We also observed that the aspects of tourism that are more profitable and in which it is worthwhile to invest in are Wildlife tourism, Beach tourism & Hunting tourism. We recommend that a lot of attention and effort, should be put into Diving and sport fishing and the other less profitable tourist attractions, to also make them worthwhile for investments and tourism

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